Overview

Dataset statistics

Number of variables20
Number of observations371528
Missing cells184008
Missing cells (%)2.5%
Duplicate rows4
Duplicate rows (%)< 0.1%
Total size in memory56.7 MiB
Average record size in memory160.0 B

Variable types

DateTime3
Text2
Categorical9
Numeric6

Alerts

nrOfPictures has constant value ""Constant
Dataset has 4 (< 0.1%) duplicate rowsDuplicates
seller is highly imbalanced (> 99.9%)Imbalance
offerType is highly imbalanced (99.9%)Imbalance
fuelType is highly imbalanced (62.6%)Imbalance
vehicleType has 37869 (10.2%) missing valuesMissing
gearbox has 20209 (5.4%) missing valuesMissing
model has 20484 (5.5%) missing valuesMissing
fuelType has 33386 (9.0%) missing valuesMissing
notRepairedDamage has 72060 (19.4%) missing valuesMissing
price is highly skewed (γ1 = 578.0590837)Skewed
yearOfRegistration is highly skewed (γ1 = 72.13364168)Skewed
powerPS is highly skewed (γ1 = 58.19990873)Skewed
price has 10778 (2.9%) zerosZeros
powerPS has 40820 (11.0%) zerosZeros
monthOfRegistration has 37675 (10.1%) zerosZeros

Reproduction

Analysis started2024-03-17 03:41:42.475863
Analysis finished2024-03-17 03:42:01.489279
Duration19.01 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Distinct280500
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2016-03-05 14:06:22
Maximum2016-04-07 14:36:58
2024-03-17T09:12:01.568744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:12:01.681992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

name
Text

Distinct233531
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:01.974297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34700
Median length51
Mean length31.993328
Min length4

Characters and Unicode

Total characters11886417
Distinct characters142
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206644 ?
Unique (%)55.6%

Sample

1st rowGolf_3_1.6
2nd rowA5_Sportback_2.7_Tdi
3rd rowJeep_Grand_Cherokee_"Overland"
4th rowGOLF_4_1_4__3TÜRER
5th rowSkoda_Fabia_1.4_TDI_PD_Classic
ValueCountFrequency (%)
opel_corsa 818
 
0.2%
ford_fiesta 779
 
0.2%
bmw_318i 632
 
0.2%
volkswagen_golf_1.4 605
 
0.2%
renault_twingo 585
 
0.2%
opel_corsa_b 534
 
0.1%
bmw_316i 531
 
0.1%
bmw_320i 494
 
0.1%
volkswagen_polo 494
 
0.1%
opel_astra 462
 
0.1%
Other values (223930) 366783
98.4%
2024-03-17T09:12:02.582426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 1861882
 
15.7%
e 799031
 
6.7%
a 598077
 
5.0%
o 503656
 
4.2%
i 486921
 
4.1%
t 463512
 
3.9%
r 441007
 
3.7%
n 434666
 
3.7%
l 350646
 
2.9%
u 331886
 
2.8%
Other values (132) 5615133
47.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6320607
53.2%
Uppercase Letter 2299790
 
19.3%
Connector Punctuation 1861882
 
15.7%
Decimal Number 1093609
 
9.2%
Other Punctuation 297037
 
2.5%
Math Symbol 8484
 
0.1%
Control 1927
 
< 0.1%
Dash Punctuation 1776
 
< 0.1%
Space Separator 888
 
< 0.1%
Modifier Symbol 228
 
< 0.1%
Other values (8) 189
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 799031
12.6%
a 598077
 
9.5%
o 503656
 
8.0%
i 486921
 
7.7%
t 463512
 
7.3%
r 441007
 
7.0%
n 434666
 
6.9%
l 350646
 
5.5%
u 331886
 
5.3%
s 315187
 
5.0%
Other values (30) 1596018
25.3%
Uppercase Letter
ValueCountFrequency (%)
T 211682
 
9.2%
A 182644
 
7.9%
V 162167
 
7.1%
S 158031
 
6.9%
C 149991
 
6.5%
M 133523
 
5.8%
D 126654
 
5.5%
P 113542
 
4.9%
I 113151
 
4.9%
B 108493
 
4.7%
Other values (28) 839912
36.5%
Other Punctuation
ValueCountFrequency (%)
. 191529
64.5%
/ 37327
 
12.6%
! 35267
 
11.9%
* 13391
 
4.5%
, 5624
 
1.9%
" 4520
 
1.5%
& 2996
 
1.0%
: 2854
 
1.0%
? 2297
 
0.8%
; 618
 
0.2%
Other values (7) 614
 
0.2%
Control
ValueCountFrequency (%)
€ 1327
68.9%
296
 
15.4%
• 144
 
7.5%
– 96
 
5.0%
“ 35
 
1.8%
„ 15
 
0.8%
Â… 6
 
0.3%
” 3
 
0.2%
Š 2
 
0.1%
1
 
0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 236238
21.6%
0 212743
19.5%
2 172877
15.8%
6 98251
9.0%
3 93274
 
8.5%
4 77150
 
7.1%
5 63547
 
5.8%
8 55672
 
5.1%
7 44172
 
4.0%
9 39685
 
3.6%
Math Symbol
ValueCountFrequency (%)
+ 6429
75.8%
| 947
 
11.2%
~ 490
 
5.8%
> 281
 
3.3%
< 216
 
2.5%
= 69
 
0.8%
× 52
 
0.6%
Modifier Symbol
ValueCountFrequency (%)
´ 137
60.1%
` 73
32.0%
^ 18
 
7.9%
Other Number
ValueCountFrequency (%)
³ 15
68.2%
² 6
 
27.3%
½ 1
 
4.5%
Other Symbol
ValueCountFrequency (%)
° 45
93.8%
® 3
 
6.2%
Currency Symbol
ValueCountFrequency (%)
$ 34
97.1%
Â¥ 1
 
2.9%
Connector Punctuation
ValueCountFrequency (%)
_ 1861882
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1776
100.0%
Space Separator
ValueCountFrequency (%)
888
100.0%
Close Punctuation
ValueCountFrequency (%)
] 32
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 32
100.0%
Final Punctuation
ValueCountFrequency (%)
» 9
100.0%
Format
ValueCountFrequency (%)
­ 7
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8620397
72.5%
Common 3266020
 
27.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 799031
 
9.3%
a 598077
 
6.9%
o 503656
 
5.8%
i 486921
 
5.6%
t 463512
 
5.4%
r 441007
 
5.1%
n 434666
 
5.0%
l 350646
 
4.1%
u 331886
 
3.9%
s 315187
 
3.7%
Other values (68) 3895808
45.2%
Common
ValueCountFrequency (%)
_ 1861882
57.0%
1 236238
 
7.2%
0 212743
 
6.5%
. 191529
 
5.9%
2 172877
 
5.3%
6 98251
 
3.0%
3 93274
 
2.9%
4 77150
 
2.4%
5 63547
 
1.9%
8 55672
 
1.7%
Other values (54) 202857
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11850215
99.7%
None 36202
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1861882
 
15.7%
e 799031
 
6.7%
a 598077
 
5.0%
o 503656
 
4.3%
i 486921
 
4.1%
t 463512
 
3.9%
r 441007
 
3.7%
n 434666
 
3.7%
l 350646
 
3.0%
u 331886
 
2.8%
Other values (83) 5578931
47.1%
None
ValueCountFrequency (%)
Ü 29724
82.1%
ë 2885
 
8.0%
€ 1327
 
3.7%
é 937
 
2.6%
Ä 361
 
1.0%
Ö 234
 
0.6%
• 144
 
0.4%
´ 137
 
0.4%
– 96
 
0.3%
× 52
 
0.1%
Other values (39) 305
 
0.8%

seller
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
privat
371525 
gewerblich
 
3

Length

Max length10
Median length6
Mean length6.0000323
Min length6

Characters and Unicode

Total characters2229180
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprivat
2nd rowprivat
3rd rowprivat
4th rowprivat
5th rowprivat

Common Values

ValueCountFrequency (%)
privat 371525
> 99.9%
gewerblich 3
 
< 0.1%

Length

2024-03-17T09:12:02.722003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:02.826263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
privat 371525
> 99.9%
gewerblich 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 371528
16.7%
i 371528
16.7%
p 371525
16.7%
v 371525
16.7%
a 371525
16.7%
t 371525
16.7%
e 6
 
< 0.1%
g 3
 
< 0.1%
w 3
 
< 0.1%
b 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2229180
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 371528
16.7%
i 371528
16.7%
p 371525
16.7%
v 371525
16.7%
a 371525
16.7%
t 371525
16.7%
e 6
 
< 0.1%
g 3
 
< 0.1%
w 3
 
< 0.1%
b 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2229180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 371528
16.7%
i 371528
16.7%
p 371525
16.7%
v 371525
16.7%
a 371525
16.7%
t 371525
16.7%
e 6
 
< 0.1%
g 3
 
< 0.1%
w 3
 
< 0.1%
b 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2229180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 371528
16.7%
i 371528
16.7%
p 371525
16.7%
v 371525
16.7%
a 371525
16.7%
t 371525
16.7%
e 6
 
< 0.1%
g 3
 
< 0.1%
w 3
 
< 0.1%
b 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

offerType
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Angebot
371516 
Gesuch
 
12

Length

Max length7
Median length7
Mean length6.9999677
Min length6

Characters and Unicode

Total characters2600684
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAngebot
2nd rowAngebot
3rd rowAngebot
4th rowAngebot
5th rowAngebot

Common Values

ValueCountFrequency (%)
Angebot 371516
> 99.9%
Gesuch 12
 
< 0.1%

Length

2024-03-17T09:12:02.923594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:03.014328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
angebot 371516
> 99.9%
gesuch 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 371528
14.3%
A 371516
14.3%
n 371516
14.3%
g 371516
14.3%
b 371516
14.3%
o 371516
14.3%
t 371516
14.3%
G 12
 
< 0.1%
s 12
 
< 0.1%
u 12
 
< 0.1%
Other values (2) 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2229156
85.7%
Uppercase Letter 371528
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 371528
16.7%
n 371516
16.7%
g 371516
16.7%
b 371516
16.7%
o 371516
16.7%
t 371516
16.7%
s 12
 
< 0.1%
u 12
 
< 0.1%
c 12
 
< 0.1%
h 12
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 371516
> 99.9%
G 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2600684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 371528
14.3%
A 371516
14.3%
n 371516
14.3%
g 371516
14.3%
b 371516
14.3%
o 371516
14.3%
t 371516
14.3%
G 12
 
< 0.1%
s 12
 
< 0.1%
u 12
 
< 0.1%
Other values (2) 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2600684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 371528
14.3%
A 371516
14.3%
n 371516
14.3%
g 371516
14.3%
b 371516
14.3%
o 371516
14.3%
t 371516
14.3%
G 12
 
< 0.1%
s 12
 
< 0.1%
u 12
 
< 0.1%
Other values (2) 24
 
< 0.1%

price
Real number (ℝ)

SKEWED  ZEROS 

Distinct5597
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17295.142
Minimum0
Maximum2.1474836 × 109
Zeros10778
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:03.117306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200
Q11150
median2950
Q37200
95-th percentile19790
Maximum2.1474836 × 109
Range2.1474836 × 109
Interquartile range (IQR)6050

Descriptive statistics

Standard deviation3587953.7
Coefficient of variation (CV)207.45443
Kurtosis345433.32
Mean17295.142
Median Absolute Deviation (MAD)2200
Skewness578.05908
Sum6.4256295 × 109
Variance1.2873412 × 1013
MonotonicityNot monotonic
2024-03-17T09:12:03.237728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10778
 
2.9%
500 5670
 
1.5%
1500 5394
 
1.5%
1000 4649
 
1.3%
1200 4594
 
1.2%
2500 4438
 
1.2%
600 3819
 
1.0%
3500 3792
 
1.0%
800 3784
 
1.0%
2000 3432
 
0.9%
Other values (5587) 321178
86.4%
ValueCountFrequency (%)
0 10778
2.9%
1 1189
 
0.3%
2 12
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%
5 26
 
< 0.1%
7 3
 
< 0.1%
8 9
 
< 0.1%
9 8
 
< 0.1%
10 84
 
< 0.1%
ValueCountFrequency (%)
2147483647 1
 
< 0.1%
99999999 15
< 0.1%
99000000 1
 
< 0.1%
74185296 1
 
< 0.1%
32545461 1
 
< 0.1%
27322222 1
 
< 0.1%
14000500 1
 
< 0.1%
12345678 9
< 0.1%
11111111 10
< 0.1%
10010011 1
 
< 0.1%

abtest
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
test
192585 
control
178943 

Length

Max length7
Median length4
Mean length5.4449221
Min length4

Characters and Unicode

Total characters2022941
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtest
2nd rowtest
3rd rowtest
4th rowtest
5th rowtest

Common Values

ValueCountFrequency (%)
test 192585
51.8%
control 178943
48.2%

Length

2024-03-17T09:12:03.345241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:03.438833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
test 192585
51.8%
control 178943
48.2%

Most occurring characters

ValueCountFrequency (%)
t 564113
27.9%
o 357886
17.7%
e 192585
 
9.5%
s 192585
 
9.5%
c 178943
 
8.8%
n 178943
 
8.8%
r 178943
 
8.8%
l 178943
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2022941
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 564113
27.9%
o 357886
17.7%
e 192585
 
9.5%
s 192585
 
9.5%
c 178943
 
8.8%
n 178943
 
8.8%
r 178943
 
8.8%
l 178943
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2022941
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 564113
27.9%
o 357886
17.7%
e 192585
 
9.5%
s 192585
 
9.5%
c 178943
 
8.8%
n 178943
 
8.8%
r 178943
 
8.8%
l 178943
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2022941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 564113
27.9%
o 357886
17.7%
e 192585
 
9.5%
s 192585
 
9.5%
c 178943
 
8.8%
n 178943
 
8.8%
r 178943
 
8.8%
l 178943
 
8.8%

vehicleType
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing37869
Missing (%)10.2%
Memory size2.8 MiB
limousine
95894 
kleinwagen
80023 
kombi
67564 
bus
30201 
cabrio
22898 
Other values (3)
37079 

Length

Max length10
Median length9
Mean length7.1582814
Min length3

Characters and Unicode

Total characters2388425
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcoupe
2nd rowsuv
3rd rowkleinwagen
4th rowkleinwagen
5th rowlimousine

Common Values

ValueCountFrequency (%)
limousine 95894
25.8%
kleinwagen 80023
21.5%
kombi 67564
18.2%
bus 30201
 
8.1%
cabrio 22898
 
6.2%
coupe 19015
 
5.1%
suv 14707
 
4.0%
andere 3357
 
0.9%
(Missing) 37869
 
10.2%

Length

2024-03-17T09:12:03.541854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:03.650356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
limousine 95894
28.7%
kleinwagen 80023
24.0%
kombi 67564
20.2%
bus 30201
 
9.1%
cabrio 22898
 
6.9%
coupe 19015
 
5.7%
suv 14707
 
4.4%
andere 3357
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 362273
15.2%
e 281669
11.8%
n 259297
10.9%
o 205371
8.6%
l 175917
7.4%
m 163458
6.8%
u 159817
6.7%
k 147587
 
6.2%
s 140802
 
5.9%
b 120663
 
5.1%
Other values (8) 371571
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2388425
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 362273
15.2%
e 281669
11.8%
n 259297
10.9%
o 205371
8.6%
l 175917
7.4%
m 163458
6.8%
u 159817
6.7%
k 147587
 
6.2%
s 140802
 
5.9%
b 120663
 
5.1%
Other values (8) 371571
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2388425
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 362273
15.2%
e 281669
11.8%
n 259297
10.9%
o 205371
8.6%
l 175917
7.4%
m 163458
6.8%
u 159817
6.7%
k 147587
 
6.2%
s 140802
 
5.9%
b 120663
 
5.1%
Other values (8) 371571
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2388425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 362273
15.2%
e 281669
11.8%
n 259297
10.9%
o 205371
8.6%
l 175917
7.4%
m 163458
6.8%
u 159817
6.7%
k 147587
 
6.2%
s 140802
 
5.9%
b 120663
 
5.1%
Other values (8) 371571
15.6%

yearOfRegistration
Real number (ℝ)

SKEWED 

Distinct155
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.578
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:03.774845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1992
Q11999
median2003
Q32008
95-th percentile2016
Maximum9999
Range8999
Interquartile range (IQR)9

Descriptive statistics

Standard deviation92.866598
Coefficient of variation (CV)0.046327256
Kurtosis5667.8597
Mean2004.578
Median Absolute Deviation (MAD)4
Skewness72.133642
Sum7.4475685 × 108
Variance8624.2049
MonotonicityNot monotonic
2024-03-17T09:12:03.893096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 24551
 
6.6%
1999 22767
 
6.1%
2005 22316
 
6.0%
2006 20230
 
5.4%
2001 20218
 
5.4%
2003 19873
 
5.3%
2004 19746
 
5.3%
2002 19189
 
5.2%
1998 17951
 
4.8%
2007 17673
 
4.8%
Other values (145) 167014
45.0%
ValueCountFrequency (%)
1000 38
< 0.1%
1001 1
 
< 0.1%
1039 1
 
< 0.1%
1111 4
 
< 0.1%
1200 1
 
< 0.1%
1234 4
 
< 0.1%
1253 1
 
< 0.1%
1255 1
 
< 0.1%
1300 2
 
< 0.1%
1400 1
 
< 0.1%
ValueCountFrequency (%)
9999 27
< 0.1%
9996 1
 
< 0.1%
9450 1
 
< 0.1%
9229 1
 
< 0.1%
9000 5
 
< 0.1%
8888 2
 
< 0.1%
8500 1
 
< 0.1%
8455 1
 
< 0.1%
8200 1
 
< 0.1%
8000 2
 
< 0.1%

gearbox
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20209
Missing (%)5.4%
Memory size2.8 MiB
manuell
274214 
automatik
77105 

Length

Max length9
Median length7
Mean length7.4389458
Min length7

Characters and Unicode

Total characters2613443
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmanuell
2nd rowmanuell
3rd rowautomatik
4th rowmanuell
5th rowmanuell

Common Values

ValueCountFrequency (%)
manuell 274214
73.8%
automatik 77105
 
20.8%
(Missing) 20209
 
5.4%

Length

2024-03-17T09:12:04.008230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:04.114815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
manuell 274214
78.1%
automatik 77105
 
21.9%

Most occurring characters

ValueCountFrequency (%)
l 548428
21.0%
a 428424
16.4%
m 351319
13.4%
u 351319
13.4%
n 274214
10.5%
e 274214
10.5%
t 154210
 
5.9%
o 77105
 
3.0%
i 77105
 
3.0%
k 77105
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2613443
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 548428
21.0%
a 428424
16.4%
m 351319
13.4%
u 351319
13.4%
n 274214
10.5%
e 274214
10.5%
t 154210
 
5.9%
o 77105
 
3.0%
i 77105
 
3.0%
k 77105
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2613443
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 548428
21.0%
a 428424
16.4%
m 351319
13.4%
u 351319
13.4%
n 274214
10.5%
e 274214
10.5%
t 154210
 
5.9%
o 77105
 
3.0%
i 77105
 
3.0%
k 77105
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2613443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 548428
21.0%
a 428424
16.4%
m 351319
13.4%
u 351319
13.4%
n 274214
10.5%
e 274214
10.5%
t 154210
 
5.9%
o 77105
 
3.0%
i 77105
 
3.0%
k 77105
 
3.0%

powerPS
Real number (ℝ)

SKEWED  ZEROS 

Distinct794
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.54948
Minimum0
Maximum20000
Zeros40820
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:04.229824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170
median105
Q3150
95-th percentile231
Maximum20000
Range20000
Interquartile range (IQR)80

Descriptive statistics

Standard deviation192.13958
Coefficient of variation (CV)1.6628338
Kurtosis4424.2988
Mean115.54948
Median Absolute Deviation (MAD)38
Skewness58.199909
Sum42929866
Variance36917.617
MonotonicityNot monotonic
2024-03-17T09:12:04.367946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40820
 
11.0%
75 24035
 
6.5%
60 15907
 
4.3%
150 15442
 
4.2%
140 13585
 
3.7%
101 13313
 
3.6%
90 12748
 
3.4%
116 11963
 
3.2%
170 10982
 
3.0%
105 10429
 
2.8%
Other values (784) 202304
54.5%
ValueCountFrequency (%)
0 40820
11.0%
1 34
 
< 0.1%
2 10
 
< 0.1%
3 9
 
< 0.1%
4 30
 
< 0.1%
5 103
 
< 0.1%
6 11
 
< 0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
20000 1
< 0.1%
19312 1
< 0.1%
19211 1
< 0.1%
19208 1
< 0.1%
17932 1
< 0.1%
17700 1
< 0.1%
17410 1
< 0.1%
17322 1
< 0.1%
17019 1
< 0.1%
17011 1
< 0.1%

model
Text

MISSING 

Distinct251
Distinct (%)0.1%
Missing20484
Missing (%)5.5%
Memory size2.8 MiB
2024-03-17T09:12:04.572884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length18
Median length15
Mean length5.059719
Min length2

Characters and Unicode

Total characters1776184
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowgolf
2nd rowgrand
3rd rowgolf
4th rowfabia
5th row3er
ValueCountFrequency (%)
golf 30070
 
8.6%
andere 26400
 
7.5%
3er 20567
 
5.9%
polo 13092
 
3.7%
corsa 12573
 
3.6%
astra 10830
 
3.1%
passat 10306
 
2.9%
a4 10257
 
2.9%
c_klasse 8775
 
2.5%
5er 8546
 
2.4%
Other values (241) 199628
56.9%
2024-03-17T09:12:04.893833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 226087
12.7%
e 214406
12.1%
r 168226
 
9.5%
o 151660
 
8.5%
s 134093
 
7.5%
l 91767
 
5.2%
t 82624
 
4.7%
i 73848
 
4.2%
n 72726
 
4.1%
c 66825
 
3.8%
Other values (27) 493922
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1634246
92.0%
Decimal Number 96705
 
5.4%
Connector Punctuation 45233
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 226087
13.8%
e 214406
13.1%
r 168226
10.3%
o 151660
9.3%
s 134093
 
8.2%
l 91767
 
5.6%
t 82624
 
5.1%
i 73848
 
4.5%
n 72726
 
4.5%
c 66825
 
4.1%
Other values (16) 351984
21.5%
Decimal Number
ValueCountFrequency (%)
3 31582
32.7%
5 13242
13.7%
4 12748
13.2%
1 10488
 
10.8%
6 8820
 
9.1%
0 7570
 
7.8%
2 5611
 
5.8%
7 2703
 
2.8%
8 2400
 
2.5%
9 1541
 
1.6%
Connector Punctuation
ValueCountFrequency (%)
_ 45233
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1634246
92.0%
Common 141938
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 226087
13.8%
e 214406
13.1%
r 168226
10.3%
o 151660
9.3%
s 134093
 
8.2%
l 91767
 
5.6%
t 82624
 
5.1%
i 73848
 
4.5%
n 72726
 
4.5%
c 66825
 
4.1%
Other values (16) 351984
21.5%
Common
ValueCountFrequency (%)
_ 45233
31.9%
3 31582
22.3%
5 13242
 
9.3%
4 12748
 
9.0%
1 10488
 
7.4%
6 8820
 
6.2%
0 7570
 
5.3%
2 5611
 
4.0%
7 2703
 
1.9%
8 2400
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1776184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 226087
12.7%
e 214406
12.1%
r 168226
 
9.5%
o 151660
 
8.5%
s 134093
 
7.5%
l 91767
 
5.2%
t 82624
 
4.7%
i 73848
 
4.2%
n 72726
 
4.1%
c 66825
 
3.8%
Other values (27) 493922
27.8%

kilometer
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125618.69
Minimum5000
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:05.000513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile30000
Q1125000
median150000
Q3150000
95-th percentile150000
Maximum150000
Range145000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation40112.337
Coefficient of variation (CV)0.31931823
Kurtosis1.2229142
Mean125618.69
Median Absolute Deviation (MAD)0
Skewness-1.5515773
Sum4.667086 × 1010
Variance1.6089996 × 109
MonotonicityNot monotonic
2024-03-17T09:12:05.095175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
150000 240797
64.8%
125000 38067
 
10.2%
100000 15920
 
4.3%
90000 12523
 
3.4%
80000 11053
 
3.0%
70000 9773
 
2.6%
60000 8669
 
2.3%
50000 7615
 
2.0%
5000 7069
 
1.9%
40000 6376
 
1.7%
Other values (3) 13666
 
3.7%
ValueCountFrequency (%)
5000 7069
1.9%
10000 1949
 
0.5%
20000 5676
1.5%
30000 6041
1.6%
40000 6376
1.7%
50000 7615
2.0%
60000 8669
2.3%
70000 9773
2.6%
80000 11053
3.0%
90000 12523
3.4%
ValueCountFrequency (%)
150000 240797
64.8%
125000 38067
 
10.2%
100000 15920
 
4.3%
90000 12523
 
3.4%
80000 11053
 
3.0%
70000 9773
 
2.6%
60000 8669
 
2.3%
50000 7615
 
2.0%
40000 6376
 
1.7%
30000 6041
 
1.6%

monthOfRegistration
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7344453
Minimum0
Maximum12
Zeros37675
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:05.192677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7124123
Coefficient of variation (CV)0.64738822
Kurtosis-1.1428356
Mean5.7344453
Median Absolute Deviation (MAD)3
Skewness0.079107888
Sum2130507
Variance13.782005
MonotonicityNot monotonic
2024-03-17T09:12:05.289288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 37675
10.1%
3 36170
9.7%
6 33167
8.9%
4 30918
8.3%
5 30631
8.2%
7 28958
7.8%
10 27337
 
7.4%
11 25489
 
6.9%
12 25380
 
6.8%
9 25074
 
6.7%
Other values (3) 70729
19.0%
ValueCountFrequency (%)
0 37675
10.1%
1 24561
6.6%
2 22403
6.0%
3 36170
9.7%
4 30918
8.3%
5 30631
8.2%
6 33167
8.9%
7 28958
7.8%
8 23765
6.4%
9 25074
6.7%
ValueCountFrequency (%)
12 25380
6.8%
11 25489
6.9%
10 27337
7.4%
9 25074
6.7%
8 23765
6.4%
7 28958
7.8%
6 33167
8.9%
5 30631
8.2%
4 30918
8.3%
3 36170
9.7%

fuelType
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing33386
Missing (%)9.0%
Memory size2.8 MiB
benzin
223857 
diesel
107746 
lpg
 
5378
cng
 
571
hybrid
 
278
Other values (2)
 
312

Length

Max length7
Median length6
Mean length5.947528
Min length3

Characters and Unicode

Total characters2011109
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbenzin
2nd rowdiesel
3rd rowdiesel
4th rowbenzin
5th rowdiesel

Common Values

ValueCountFrequency (%)
benzin 223857
60.3%
diesel 107746
29.0%
lpg 5378
 
1.4%
cng 571
 
0.2%
hybrid 278
 
0.1%
andere 208
 
0.1%
elektro 104
 
< 0.1%
(Missing) 33386
 
9.0%

Length

2024-03-17T09:12:05.405569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:05.509165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
benzin 223857
66.2%
diesel 107746
31.9%
lpg 5378
 
1.6%
cng 571
 
0.2%
hybrid 278
 
0.1%
andere 208
 
0.1%
elektro 104
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 448493
22.3%
e 439973
21.9%
i 331881
16.5%
b 224135
11.1%
z 223857
11.1%
l 113228
 
5.6%
d 108232
 
5.4%
s 107746
 
5.4%
g 5949
 
0.3%
p 5378
 
0.3%
Other values (8) 2237
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2011109
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 448493
22.3%
e 439973
21.9%
i 331881
16.5%
b 224135
11.1%
z 223857
11.1%
l 113228
 
5.6%
d 108232
 
5.4%
s 107746
 
5.4%
g 5949
 
0.3%
p 5378
 
0.3%
Other values (8) 2237
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2011109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 448493
22.3%
e 439973
21.9%
i 331881
16.5%
b 224135
11.1%
z 223857
11.1%
l 113228
 
5.6%
d 108232
 
5.4%
s 107746
 
5.4%
g 5949
 
0.3%
p 5378
 
0.3%
Other values (8) 2237
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2011109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 448493
22.3%
e 439973
21.9%
i 331881
16.5%
b 224135
11.1%
z 223857
11.1%
l 113228
 
5.6%
d 108232
 
5.4%
s 107746
 
5.4%
g 5949
 
0.3%
p 5378
 
0.3%
Other values (8) 2237
 
0.1%

brand
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
volkswagen
79640 
bmw
40274 
opel
40136 
mercedes_benz
35309 
audi
32873 
Other values (35)
143296 

Length

Max length14
Median length13
Mean length6.7532864
Min length3

Characters and Unicode

Total characters2509035
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvolkswagen
2nd rowaudi
3rd rowjeep
4th rowvolkswagen
5th rowskoda

Common Values

ValueCountFrequency (%)
volkswagen 79640
21.4%
bmw 40274
10.8%
opel 40136
10.8%
mercedes_benz 35309
9.5%
audi 32873
8.8%
ford 25573
 
6.9%
renault 17969
 
4.8%
peugeot 11027
 
3.0%
fiat 9676
 
2.6%
seat 7022
 
1.9%
Other values (30) 72029
19.4%

Length

2024-03-17T09:12:05.625387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
volkswagen 79640
21.4%
bmw 40274
10.8%
opel 40136
10.8%
mercedes_benz 35309
9.5%
audi 32873
8.8%
ford 25573
 
6.9%
renault 17969
 
4.8%
peugeot 11027
 
3.0%
fiat 9676
 
2.6%
seat 7022
 
1.9%
Other values (30) 72029
19.4%

Most occurring characters

ValueCountFrequency (%)
e 330339
 
13.2%
a 207305
 
8.3%
o 205135
 
8.2%
s 169113
 
6.7%
n 163877
 
6.5%
l 148193
 
5.9%
w 120456
 
4.8%
d 114816
 
4.6%
r 103102
 
4.1%
m 95327
 
3.8%
Other values (15) 851372
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2466629
98.3%
Connector Punctuation 42406
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 330339
13.4%
a 207305
 
8.4%
o 205135
 
8.3%
s 169113
 
6.9%
n 163877
 
6.6%
l 148193
 
6.0%
w 120456
 
4.9%
d 114816
 
4.7%
r 103102
 
4.2%
m 95327
 
3.9%
Other values (14) 808966
32.8%
Connector Punctuation
ValueCountFrequency (%)
_ 42406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2466629
98.3%
Common 42406
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 330339
13.4%
a 207305
 
8.4%
o 205135
 
8.3%
s 169113
 
6.9%
n 163877
 
6.6%
l 148193
 
6.0%
w 120456
 
4.9%
d 114816
 
4.7%
r 103102
 
4.2%
m 95327
 
3.9%
Other values (14) 808966
32.8%
Common
ValueCountFrequency (%)
_ 42406
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2509035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 330339
 
13.2%
a 207305
 
8.3%
o 205135
 
8.2%
s 169113
 
6.7%
n 163877
 
6.5%
l 148193
 
5.9%
w 120456
 
4.8%
d 114816
 
4.6%
r 103102
 
4.1%
m 95327
 
3.8%
Other values (15) 851372
33.9%

notRepairedDamage
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing72060
Missing (%)19.4%
Memory size2.8 MiB
nein
263182 
ja
36286 

Length

Max length4
Median length4
Mean length3.7576636
Min length2

Characters and Unicode

Total characters1125300
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowja
2nd rownein
3rd rownein
4th rowja
5th rownein

Common Values

ValueCountFrequency (%)
nein 263182
70.8%
ja 36286
 
9.8%
(Missing) 72060
 
19.4%

Length

2024-03-17T09:12:05.738643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:05.829968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
nein 263182
87.9%
ja 36286
 
12.1%

Most occurring characters

ValueCountFrequency (%)
n 526364
46.8%
e 263182
23.4%
i 263182
23.4%
j 36286
 
3.2%
a 36286
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1125300
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 526364
46.8%
e 263182
23.4%
i 263182
23.4%
j 36286
 
3.2%
a 36286
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1125300
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 526364
46.8%
e 263182
23.4%
i 263182
23.4%
j 36286
 
3.2%
a 36286
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1125300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 526364
46.8%
e 263182
23.4%
i 263182
23.4%
j 36286
 
3.2%
a 36286
 
3.2%
Distinct114
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2014-03-10 00:00:00
Maximum2016-04-07 00:00:00
2024-03-17T09:12:05.926882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:12:06.051946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

nrOfPictures
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
0
371528 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters371528
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 371528
100.0%

Length

2024-03-17T09:12:06.173041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T09:12:06.252606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 371528
100.0%

Most occurring characters

ValueCountFrequency (%)
0 371528
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 371528
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 371528
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 371528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 371528
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 371528
100.0%

postalCode
Real number (ℝ)

Distinct8150
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50820.668
Minimum1067
Maximum99998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-17T09:12:06.348919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1067
5-th percentile9661
Q130459
median49610
Q371546
95-th percentile93133
Maximum99998
Range98931
Interquartile range (IQR)41087

Descriptive statistics

Standard deviation25799.082
Coefficient of variation (CV)0.50764942
Kurtosis-0.97577938
Mean50820.668
Median Absolute Deviation (MAD)20731
Skewness0.06188007
Sum1.8881301 × 1010
Variance6.6559266 × 108
MonotonicityNot monotonic
2024-03-17T09:12:06.642492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10115 828
 
0.2%
65428 637
 
0.2%
66333 349
 
0.1%
38518 326
 
0.1%
44145 323
 
0.1%
32257 323
 
0.1%
52525 314
 
0.1%
78224 309
 
0.1%
26789 301
 
0.1%
48599 294
 
0.1%
Other values (8140) 367524
98.9%
ValueCountFrequency (%)
1067 96
< 0.1%
1068 1
 
< 0.1%
1069 59
< 0.1%
1097 29
 
< 0.1%
1099 67
< 0.1%
1108 12
 
< 0.1%
1109 80
< 0.1%
1127 31
 
< 0.1%
1129 46
< 0.1%
1139 67
< 0.1%
ValueCountFrequency (%)
99998 16
 
< 0.1%
99996 3
 
< 0.1%
99994 7
 
< 0.1%
99991 2
 
< 0.1%
99988 9
 
< 0.1%
99986 19
 
< 0.1%
99976 37
 
< 0.1%
99974 159
< 0.1%
99958 9
 
< 0.1%
99955 23
 
< 0.1%
Distinct182806
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2016-03-05 14:15:08
Maximum2016-04-07 14:58:51
2024-03-17T09:12:06.760700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:12:06.888010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-03-17T09:11:58.529103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:54.890026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.611315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.315354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.042654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.788657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.643451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.015851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.724267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.430638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.171709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.915054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.762193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.129862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.833895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.547952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.289937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.032939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.894438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.258834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.960153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.678171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.423070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.159642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:59.016649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.379848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.081278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.807085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.548568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.283202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:59.136659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:55.492744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.199432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:56.929971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:57.670914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T09:11:58.407937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-17T09:11:59.385276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-17T09:11:59.966170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dateCrawlednamesellerofferTypepriceabtestvehicleTypeyearOfRegistrationgearboxpowerPSmodelkilometermonthOfRegistrationfuelTypebrandnotRepairedDamagedateCreatednrOfPicturespostalCodelastSeen
02016-03-24 11:52:17Golf_3_1.6privatAngebot480testNaN1993manuell0golf1500000benzinvolkswagenNaN2016-03-24 00:00:000704352016-04-07 03:16:57
12016-03-24 10:58:45A5_Sportback_2.7_TdiprivatAngebot18300testcoupe2011manuell190NaN1250005dieselaudija2016-03-24 00:00:000669542016-04-07 01:46:50
22016-03-14 12:52:21Jeep_Grand_Cherokee_"Overland"privatAngebot9800testsuv2004automatik163grand1250008dieseljeepNaN2016-03-14 00:00:000904802016-04-05 12:47:46
32016-03-17 16:54:04GOLF_4_1_4__3TÜRERprivatAngebot1500testkleinwagen2001manuell75golf1500006benzinvolkswagennein2016-03-17 00:00:000910742016-03-17 17:40:17
42016-03-31 17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivatAngebot3600testkleinwagen2008manuell69fabia900007dieselskodanein2016-03-31 00:00:000604372016-04-06 10:17:21
52016-04-04 17:36:23BMW_316i___e36_Limousine___Bastlerfahrzeug__ExportprivatAngebot650testlimousine1995manuell1023er15000010benzinbmwja2016-04-04 00:00:000337752016-04-06 19:17:07
62016-04-01 20:48:51Peugeot_206_CC_110_PlatinumprivatAngebot2200testcabrio2004manuell1092_reihe1500008benzinpeugeotnein2016-04-01 00:00:000671122016-04-05 18:18:39
72016-03-21 18:54:38VW_Derby_Bj_80__ScheunenfundprivatAngebot0testlimousine1980manuell50andere400007benzinvolkswagennein2016-03-21 00:00:000193482016-03-25 16:47:58
82016-04-04 23:42:13Ford_C___Max_Titanium_1_0_L_EcoBoostprivatAngebot14500controlbus2014manuell125c_max300008benzinfordNaN2016-04-04 00:00:000945052016-04-04 23:42:13
92016-03-17 10:53:50VW_Golf_4_5_tuerig_zu_verkaufen_mit_AnhaengerkupplungprivatAngebot999testkleinwagen1998manuell101golf1500000NaNvolkswagenNaN2016-03-17 00:00:000274722016-03-31 17:17:06
dateCrawlednamesellerofferTypepriceabtestvehicleTypeyearOfRegistrationgearboxpowerPSmodelkilometermonthOfRegistrationfuelTypebrandnotRepairedDamagedateCreatednrOfPicturespostalCodelastSeen
3715182016-04-02 20:37:03Bmw_320_D_DPF_Touring_!!!privatAngebot3999testkombi2005manuell33er1500005dieselbmwnein2016-04-02 00:00:000818252016-04-06 20:47:12
3715192016-03-09 13:37:43Alfa_Romeo_159_Jtdm_1.9_150_ps_13_600_km_top_vollprivatAngebot5250controlNaN2016automatik15015915000012NaNalfa_romeonein2016-03-09 00:00:000513712016-03-13 01:44:13
3715202016-03-19 19:53:49turbo_defektprivatAngebot3200controllimousine2004manuell225leon1500005benzinseatja2016-03-19 00:00:000964652016-03-19 20:44:43
3715212016-03-27 20:36:20Opel_Zafira_1.6_Elegance_TÜV_12/16privatAngebot1150controlbus2000manuell0zafira1500003benzinopelnein2016-03-27 00:00:000266242016-03-29 10:17:23
3715222016-03-21 09:50:58Mitsubishi_ColdprivatAngebot0controlNaN2005manuell0colt1500007benzinmitsubishija2016-03-21 00:00:00026942016-03-21 10:42:49
3715232016-03-14 17:48:27Suche_t4___vito_ab_6_sitzeprivatAngebot2200testNaN2005NaN0NaN200001NaNsonstige_autosNaN2016-03-14 00:00:000395762016-04-06 00:46:52
3715242016-03-05 19:56:21Smart_smart_leistungssteigerung_100psprivatAngebot1199testcabrio2000automatik101fortwo1250003benzinsmartnein2016-03-05 00:00:000261352016-03-11 18:17:12
3715252016-03-19 18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privatAngebot9200testbus1996manuell102transporter1500003dieselvolkswagennein2016-03-19 00:00:000874392016-04-07 07:15:26
3715262016-03-20 19:41:08VW_Golf_Kombi_1_9l_TDIprivatAngebot3400testkombi2002manuell100golf1500006dieselvolkswagenNaN2016-03-20 00:00:000407642016-03-24 12:45:21
3715272016-03-07 19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivatAngebot28990controllimousine2013manuell320m_reihe500008benzinbmwnein2016-03-07 00:00:000733262016-03-22 03:17:10

Duplicate rows

Most frequently occurring

dateCrawlednamesellerofferTypepriceabtestvehicleTypeyearOfRegistrationgearboxpowerPSmodelkilometermonthOfRegistrationfuelTypebrandnotRepairedDamagedateCreatednrOfPicturespostalCodelastSeen# duplicates
02016-03-08 18:42:48Mercedes_Benz_CLK_Coupe_230_Kompressor_SportprivatAngebot1799testcoupe1999automatik193clk200007benzinmercedes_benznein2016-03-08 00:00:000895182016-03-09 09:46:572
12016-03-18 18:46:15Volkswagen_Passat_Variant_1.9_TDI_HighlineprivatAngebot1999controlkombi2001manuell131passat1500007dieselvolkswagennein2016-03-18 00:00:000363912016-03-18 18:46:152
22016-03-28 00:56:10Suzuki_IgnisprivatAngebot1000controlkleinwagen2002manuell83andere1500001benzinsuzukinein2016-03-28 00:00:000665892016-03-28 08:46:212
32016-04-03 09:01:15Mercedes_Benz_CLK_320_W209privatAngebot4699testcoupe2003automatik218clk1250006benzinmercedes_benzja2016-04-03 00:00:000751962016-04-07 09:44:542